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          <section class="tex2jax_ignore mathjax_ignore" id="demos">
<h1>Demos<a class="headerlink" href="#demos" title="Link to this heading">¶</a></h1>
<!-- freshness: { owner: 'lit-dev' reviewed: '2024-06-24' } -->
<!-- [TOC] placeholder - DO NOT REMOVE -->
<p>The LIT team maintains a number of hosted demos, as well as pre-built launchers
for some common tasks and model types.</p>
<p>For publicly-visible demos hosted on Google Cloud, see
<a class="reference external" href="https://pair-code.github.io/lit/demos/">https://pair-code.github.io/lit/demos/</a>.</p>
<hr class="docutils" />
<section id="classification">
<h2>Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --><a class="headerlink" href="#classification" title="Link to this heading">¶</a></h2>
<section id="sentiment-and-nli">
<h3>Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --><a class="headerlink" href="#sentiment-and-nli" title="Link to this heading">¶</a></h3>
<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html">https://pair-code.github.io/lit/demos/glue.html</a> <br />
<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a></p>
<ul class="simple">
<li><p>Multi-task demo:</p>
<ul>
<li><p>Sentiment analysis as a binary classification task
(<a class="reference external" href="https://nlp.stanford.edu/sentiment/treebank.html">SST-2</a>) on single
sentences.</p></li>
<li><p>Natural Language Inference (NLI) using
<a class="reference external" href="https://cims.nyu.edu/~sbowman/multinli/">MultiNLI</a>, as a three-way
classification task with two-segment input (premise, hypothesis).</p></li>
<li><p>STS-B textual similarity task (see
<a class="reference internal" href="#regression-scoring"><span class="xref myst">Regression / Scoring</span></a> below).</p></li>
<li><p>Switch tasks using the Settings (⚙️) menu.</p></li>
</ul>
</li>
<li><p>BERT models of different sizes, built on HuggingFace TF2 (Keras).</p></li>
<li><p>Supports the widest range of LIT interpretability features:</p>
<ul>
<li><p>Model output probabilities, custom thresholds, and multiclass metrics.</p></li>
<li><p>Jitter plot of output scores, to find confident examples or ones near
the margin.</p></li>
<li><p>Embedding projector to find clusters in representation space.</p></li>
<li><p>Integrated Gradients, LIME, and other salience methods.</p></li>
<li><p>Counterfactual generators, including HotFlip for targeted adversarial
perturbations.</p></li>
</ul>
</li>
</ul>
<p>Tip: check out a case study for this demo on the public LIT website:
<a class="reference external" href="https://pair-code.github.io/lit/tutorials/sentiment">https://pair-code.github.io/lit/tutorials/sentiment</a></p>
</section>
</section>
<hr class="docutils" />
<section id="regression-scoring">
<h2>Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --><a class="headerlink" href="#regression-scoring" title="Link to this heading">¶</a></h2>
<section id="textual-similarity-sts-b">
<h3>Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --><a class="headerlink" href="#textual-similarity-sts-b" title="Link to this heading">¶</a></h3>
<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/glue.html?models=stsb&amp;amp;dataset=stsb_dev">https://pair-code.github.io/lit/demos/glue.html?models=stsb&amp;dataset=stsb_dev</a> <br />
<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py">examples/glue/demo.py</a></p>
<ul class="simple">
<li><p>STS-B textual similarity task, predicting scores on a range from 0
(unrelated) to 5 (very similar).</p></li>
<li><p>BERT models built on HuggingFace TF2 (Keras).</p></li>
<li><p>Supports a wide range of LIT interpretability features:</p>
<ul>
<li><p>Model output scores and metrics.</p></li>
<li><p>Scatter plot of scores and error, and jitter plot of true labels for
quick filtering.</p></li>
<li><p>Embedding projector to find clusters in representation space.</p></li>
<li><p>Integrated Gradients, LIME, and other salience methods.</p></li>
</ul>
</li>
</ul>
</section>
</section>
<hr class="docutils" />
<section id="sequence-to-sequence">
<h2>Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --><a class="headerlink" href="#sequence-to-sequence" title="Link to this heading">¶</a></h2>
<section id="gemma">
<h3>Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --><a class="headerlink" href="#gemma" title="Link to this heading">¶</a></h3>
<p><strong>Code:</strong>
<a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py">examples/prompt_debugging/server.py</a></p>
<ul class="simple">
<li><p>Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
and Transformers (with PyTorch).</p></li>
<li><p>Interactively debug LLM prompts using
<a class="reference internal" href="components.html#sequence-salience"><span class="std std-ref">sequence salience</span></a>.</p></li>
<li><p>Multiple salience methods (grad-l2 and grad-dot-input), at multiple
granularities: token-, word-, line-, sentence-, and paragraph-level.</p></li>
</ul>
<p>Tip: check out the in-depth walkthrough at
<a class="reference external" href="https://ai.google.dev/responsible/model_behavior">https://ai.google.dev/responsible/model_behavior</a>, part of the Responsible
Generative AI Toolkit.</p>
</section>
</section>
<hr class="docutils" />
<section id="multimodal">
<h2>Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --><a class="headerlink" href="#multimodal" title="Link to this heading">¶</a></h2>
<section id="tabular-data-penguin-classification">
<h3>Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --><a class="headerlink" href="#tabular-data-penguin-classification" title="Link to this heading">¶</a></h3>
<p><strong>Hosted instance:</strong> <a class="reference external" href="https://pair-code.github.io/lit/demos/penguins.html">https://pair-code.github.io/lit/demos/penguins.html</a> <br />
<strong>Code:</strong> <a class="reference external" href="https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py">examples/penguin/demo.py</a></p>
<ul class="simple">
<li><p>Binary classification on
<a class="reference external" href="https://www.tensorflow.org/datasets/catalog/penguins">penguin dataset</a>.</p></li>
<li><p>Showing using of LIT on non-text data (numeric and categorical features).</p></li>
<li><p>Use partial-dependence plots to understand feature importance on individual
examples, selections, or the entire evaluation dataset.</p></li>
<li><p>Use binary classifier threshold setters to find best thresholds for slices
of examples to achieve specific fairness constraints, such as demographic
parity.</p></li>
</ul>
</section>
</section>
</section>

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<li><a class="reference internal" href="#">Demos</a><ul>
<li><a class="reference internal" href="#classification">Classification <!-- DO NOT REMOVE {#classification .demo-section-header} --></a><ul>
<li><a class="reference internal" href="#sentiment-and-nli">Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} --></a></li>
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<li><a class="reference internal" href="#regression-scoring">Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} --></a><ul>
<li><a class="reference internal" href="#textual-similarity-sts-b">Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} --></a></li>
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<li><a class="reference internal" href="#sequence-to-sequence">Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} --></a><ul>
<li><a class="reference internal" href="#gemma">Gemma <!-- DO NOT REMOVE {#gemma .demo-header} --></a></li>
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<li><a class="reference internal" href="#multimodal">Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} --></a><ul>
<li><a class="reference internal" href="#tabular-data-penguin-classification">Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} --></a></li>
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